This application describes two hypothesis driven projects. In brief, the first hypothesis is that the enthalpy and entropy of molecular recognition events can be correlated to solvation parameters, buried surface area, and electrostatic interactions between the hosts and guests. The correlations may be as simple as linear relationships, but they are expected to be more complex, possibly requiring artificial neural networks. The second hypothesis is that cooperative binding will follow a compensating enthalpy/entropy relationship that is influenced by differences in solvation effects, buried surface area, and the molecular recognition contacts involved. In Project 1 we will use three classes of synthetic receptors: Anslyn group receptors, Schneider group receptors, and cyclodextrins. We will measure the enthalpy and entropy of binding of a series of guests, measure solvation parameters of the guests, computationally model buried surface area upon binding, and determine the electrostatic contacts created upon binding. The data will be used as training sets for pattern recognition routines to predict the enthalpy and entropy of binding to guests not used in the training sets. In Project 2 cooperativity between three different binding forces in water will be studied: ion-pairing, metal-chelation, and the hydrophobic effect. We define three guests, A, B and AB, where A and B are subunits of AB. Our studies will involve ITC analyses of the binding of A, B, and AB to a host. Using mathematical relationships we derive herein, the results from the ITC studies will give the enthalpy or entropy origin of the various binding events. Because the guests are synthetic and structurally simple, several can be quickly generated to reveal varying degrees of negative and positive cooperativity. After examining the enthalpy and entropy of binding A, B, and AB for the series of guests, a compensation relationship for the enthalpy/entropy of connection will be generated (Eq. 7 herein). Lastly, the compensating enthalpy and entropy of connection will then be correlated with solvation parameters, buried surface area, and electrostatic contacts, tying together projects 1 and 2, and revealing the chemical basis for cooperative binding.
The work described herein focuses upon two aspects of the physical organic chemistry of molecular recognition. The first deals with creating protocols to predict binding affinities, while the second involves exploring methods to achieve positive cooperativity.
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